学习opencv 第六章 习题十三

#include "stdafx.h"
#include "cv.h"
#include "highgui.h"
#include <iostream>

using namespace cv;
using namespace std;

void speedy_convolution(const CvMat* A,const CvMat* B,CvMat* C);

int main()
{
IplImage* img=cvLoadImage("C:/Users/shark/Desktop/fruits.jpg",0);
CvMat* src=cvCreateMat(img->height,img->width,CV_32FC1);
/*int data;
for(int i=0;i<img->height;i++)
{
for(int j=0;j<img->width;j++)
{
data=img->imageData[i*img->widthStep+j];
cvmSet(src,i,j,data);
}
}*/
//必须归一化矩阵的值为0-1之间(缩放比例在1/255.0附近效果最好,太小最后会全黑,接近1或大于1几乎是全白;
//(还未深入了解函数cvConvertScale的机理),缩放比例不能为1,打出目标图像的像素有正有负
cvConvertScale(img,src,1/255.0,0);

CvMat* kernel=cvCreateMat(3,3,CV_32FC1);
cvSetReal2D(kernel,0,0,1.0/16);    cvSetReal2D(kernel,0,1,2.0/16); cvSetReal2D(kernel,0,2,1.0/16);    //注意设置值时必须加个.0否则1/16的值0
cvSetReal2D(kernel,1,0,2.0/16);    cvSetReal2D(kernel,1,1,4.0/16); cvSetReal2D(kernel,1,2,2.0/16);
cvSetReal2D(kernel,2,0,1.0/16);    cvSetReal2D(kernel,2,1,2.0/16); cvSetReal2D(kernel,2,2,1.0/16);
CvMat* C=cvCreateMat((src->rows+kernel->rows-1),(src->cols+kernel->cols-1),src->type);
speedy_convolution(src,kernel,C);

IplImage* img_src=cvCreateImage(cvGetSize(src),IPL_DEPTH_32F,1);
cvGetImage(src,img_src);
IplImage* img_dst=cvCreateImage(cvGetSize(C),IPL_DEPTH_32F,1);
cvGetImage(C,img_dst);

cvNamedWindow("img_src");
cvShowImage("img_src",img_src);
cvNamedWindow("img");
cvShowImage("img",img);
cvNamedWindow("dst");
cvShowImage("dst",img_dst);
cvWaitKey();
return 0;
}

void speedy_convolution(
const CvMat* A,
const CvMat* B,
CvMat* C
){
int dft_M=cvGetOptimalDFTSize(A->rows+B->rows-1);
int dft_N=cvGetOptimalDFTSize(A->cols+B->cols-1);

CvMat *dft_A=cvCreateMat(dft_M,dft_N,A->type);
CvMat *dft_B=cvCreateMat(dft_M,dft_N,B->type);
CvMat tmp;
cvGetSubRect(dft_A,&tmp,cvRect(0,0,A->cols,A->rows));
cvCopy(A,&tmp);
cvGetSubRect(dft_A,&tmp,cvRect(A->cols,0,dft_A->cols-A->cols,A->rows));
cvZero(&tmp);
cvDFT(dft_A,dft_A,CV_DXT_FORWARD,A->rows);

cvGetSubRect(dft_B,&tmp,cvRect(0,0,B->cols,B->rows));
cvCopy(B,&tmp);
cvGetSubRect(dft_B,&tmp,cvRect(B->cols,0,dft_B->cols-B->cols,B->rows));
cvZero(&tmp);
cvDFT(dft_B,dft_B,CV_DXT_FORWARD,B->rows);

cvMulSpectrums(dft_A,dft_B,dft_A,0);

cvDFT(dft_A,dft_A,CV_DXT_INV_SCALE,C->rows);
cvGetSubRect(dft_A,&tmp,cvRect(0,0,C->cols,C->rows));
cvCopy(&tmp,C);
cvReleaseMat(&dft_A);
cvReleaseMat(&dft_B);
}

#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>

using namespace cv;
using namespace std;

//http://docs.opencv.org/modules/core/doc/operations_on_arrays.html#dft[2]
void convolveDFT(Mat A, Mat B, Mat& C)
{
// reallocate the output array if needed
C.create(abs(A.rows - B.rows)+1, abs(A.cols - B.cols)+1, A.type());
Size dftSize;
// calculate the size of DFT transform
dftSize.width = getOptimalDFTSize(A.cols + B.cols - 1);
dftSize.height = getOptimalDFTSize(A.rows + B.rows - 1);

// allocate temporary buffers and initialize them with 0's
Mat tempA(dftSize, A.type(), Scalar::all(0));
Mat tempB(dftSize, B.type(), Scalar::all(0));

// copy A and B to the top-left corners of tempA and tempB, respectively
Mat roiA(tempA, Rect(0,0,A.cols,A.rows));
A.copyTo(roiA);
Mat roiB(tempB, Rect(0,0,B.cols,B.rows));
B.copyTo(roiB);

// now transform the padded A & B in-place;
// use "nonzeroRows" hint for faster processing
dft(tempA, tempA, 0, A.rows);
dft(tempB, tempB, 0, B.rows);

// multiply the spectrums;
// the function handles packed spectrum representations well
mulSpectrums(tempA, tempB, tempA, DFT_COMPLEX_OUTPUT);
//mulSpectrums(tempA, tempB, tempA, DFT_REAL_OUTPUT);

// transform the product back from the frequency domain.
// Even though all the result rows will be non-zero,
// you need only the first C.rows of them, and thus you
// pass nonzeroRows == C.rows
dft(tempA, tempA, DFT_INVERSE + DFT_SCALE, C.rows);

// now copy the result back to C.
tempA(Rect(0, 0, C.cols, C.rows)).copyTo(C);

// all the temporary buffers will be deallocated automatically
}

int main(int argc, char* argv[])
{
const char* filename = argc >=2 ? argv[1] : "Lenna.png";

Mat I = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
if( I.empty())
return -1;

Mat kernel = (Mat_<float>(3,3) << 1, 1, 1, 1, 1, 1, 1, 1, 1);
cout << kernel;

Mat floatI = Mat_<float>(I);// change image type into float
Mat filteredI;
convolveDFT(floatI, kernel, filteredI);

normalize(filteredI, filteredI, 0, 1, CV_MINMAX); // Transform the matrix with float values into a
// viewable image form (float between values 0 and 1).
imshow("image", I);
imshow("filtered", filteredI);
waitKey(0);

}

//一是输出Mat C应声明为引用；二是其中的mulSpectrums函数的第四个参数flag值没有指定，应指定为DFT_COMPLEX_OUTPUT或是DFT_REAL_OUTPUT.

//main函数中首先按灰度图读入图像，然后创造一个平滑核kernel，将输入图像转换成float类型(注意这步是必须的，因为dft只能处理浮点数)，在调用convolveDFT求出卷积结果后，将卷积结果归一化方便显示观看。

//需要注意的是，一般求法中，利用核游走整个图像进行卷积运算，实际上进行的是相关运算，真正意义上的卷积，应该首先把核翻转180度，再在整个图像上进行游走。OpenCV中的filter2D实际上做的也只是相关，而非卷积。